2022
DOI: 10.48550/arxiv.2201.11192
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery

Abstract: Forest biomass is a key influence for future climate, and the world urgently needs highly scalable financing schemes, such as carbon offsetting certifications, to protect and restore forests. Current manual forest carbon stock inventory methods of measuring single trees by hand are time, labour, and cost intensive and have been shown to be subjective. They can lead to substantial overestimation of the carbon stock and ultimately distrust in forest financing. The potential for impact and scale of leveraging adv… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2023
2023
2023
2023

Publication Types

Select...
1
1

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(2 citation statements)
references
References 28 publications
(20 reference statements)
0
2
0
Order By: Relevance
“…Reiersen et al have developed a database named ReforesTree that includes data on carbon stocks in some forests in Ecuador [138] . The project aims to overcome the carbon deficiency in some interested forests.…”
Section: Climate and Environmental Applicationsmentioning
confidence: 99%
“…Reiersen et al have developed a database named ReforesTree that includes data on carbon stocks in some forests in Ecuador [138] . The project aims to overcome the carbon deficiency in some interested forests.…”
Section: Climate and Environmental Applicationsmentioning
confidence: 99%
“…In recent years, significant progress has been made in this field of machine learning-based feature extraction and AGB estimation [16,37,18]. One of the most promising approaches in this field is the use of deep supervised learning, evolving into a widely adopted approach in forestry research in general, [38,39,40], and in AGB estimation in particular [41,42]. Deep learning (DL), as a class of machine learning algorithms, gained popularity due to its ability to automatically extract features from raw data, leading to state-of-the-art performance in various fields such as computer vision, [43] requiring large amounts of labeled data.…”
Section: Introductionmentioning
confidence: 99%